from sklearn_benchmarks.reporting.hp_match import HPMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HPMatchReporting(against_lib="sklearnex", config="config.yml")
reporting.make_report()

We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=brute.

WARNING! Mismatch between validation scores for 14 predictions (58.33%). See below.

	estimator	function	n_samples_train	n_samples	n_features	accuracy_score_sklearn	accuracy_score_sklearnex	diff_accuracy_scores
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.663	0.687	0.024
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.757	0.742	0.015
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.882	0.875	0.007
8	KNeighborsClassifier_brute_force	predict	100000	1	100	1.000	0.000	1.000
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.882	0.875	0.007
11	KNeighborsClassifier_brute_force	predict	100000	1	100	1.000	0.000	1.000
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.757	0.742	0.015
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	0.663	0.687	0.024
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.896	0.967	0.071
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.922	0.974	0.052
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.929	0.975	0.046
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.929	0.975	0.046
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.922	0.974	0.052
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	0.896	0.967	0.071

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	7.538	0.0	-1	1	0.045	0.001	0.237	0.237	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	7.557	0.0	-1	5	0.045	0.000	0.234	0.234	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	7.571	0.0	1	100	0.045	0.000	0.237	0.237	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.010	0.0	7.644	0.0	-1	100	0.045	0.000	0.232	0.232	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	7.590	0.0	1	5	0.045	0.001	0.237	0.237	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.010	0.0	7.643	0.0	1	1	0.046	0.000	0.230	0.230	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	0.420	0.0	-1	1	0.007	0.000	0.514	0.514	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	0.376	0.0	-1	5	0.007	0.000	0.573	0.573	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	0.373	0.0	1	100	0.008	0.000	0.566	0.567	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	0.376	0.0	-1	100	0.008	0.000	0.565	0.565	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	0.372	0.0	1	5	0.008	0.000	0.570	0.571	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	0.377	0.0	1	1	0.007	0.000	0.569	0.569	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.070	0.234	0.000	0.002	-1	1	0.184	0.013	11.233	11.259	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.021	0.002	0.000	0.021	-1	1	0.008	0.000	2.431	2.431	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.721	0.078	0.000	0.003	-1	5	0.176	0.003	15.418	15.421	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.022	0.003	0.000	0.022	-1	5	0.008	0.000	2.591	2.592	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.906	0.009	0.000	0.002	1	100	0.210	0.002	9.086	9.086	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.019	0.000	0.000	0.019	1	100	0.008	0.000	2.244	2.244	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.701	0.042	0.000	0.003	-1	100	0.208	0.002	12.958	12.958	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.021	0.002	0.000	0.021	-1	100	0.009	0.001	2.473	2.486	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.882	0.004	0.000	0.002	1	5	0.195	0.040	9.656	9.860	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.019	0.000	0.000	0.019	1	5	0.008	0.000	2.235	2.236	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.150	0.004	0.001	0.001	1	1	0.171	0.003	6.708	6.709	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.018	0.000	0.000	0.018	1	1	0.008	0.000	2.175	2.175	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.783	0.036	0.000	0.002	-1	1	0.025	0.001	71.166	71.182	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.005	0.004	0.000	0.005	-1	1	0.001	0.000	8.281	8.307	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.628	0.077	0.000	0.003	-1	5	0.027	0.000	98.627	98.644	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.009	0.005	0.000	0.009	-1	5	0.001	0.000	14.185	14.300	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.951	0.004	0.000	0.002	1	100	0.060	0.003	32.355	32.387	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	100	0.001	0.000	3.889	3.901	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.653	0.074	0.000	0.003	-1	100	0.059	0.001	45.061	45.063	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.006	0.002	0.000	0.006	-1	100	0.001	0.000	7.720	7.772	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.937	0.002	0.000	0.002	1	5	0.027	0.001	73.067	73.134	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	5	0.001	0.000	4.573	4.591	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.063	0.004	0.000	0.001	1	1	0.025	0.000	42.060	42.067	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	0.000	0.002	1	1	0.001	0.000	2.800	2.806	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=kd_tree.

WARNING! Mismatch between validation scores for 12 predictions (50.0%). See below.

	estimator	function	n_samples_train	n_samples	n_features	accuracy_score_sklearn	accuracy_score_sklearnex	diff_accuracy_scores
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.929	0.910	0.019
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.946	0.941	0.005
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.951	0.940	0.011
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.951	0.940	0.011
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.946	0.941	0.005
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.929	0.910	0.019
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.891	0.879	0.012
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.911	0.905	0.006
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.894	0.917	0.023
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.894	0.917	0.023
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.911	0.905	0.006
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.891	0.879	0.012

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2.745	0.028	0.029	0.0	-1	1	0.765	0.046	3.590	3.597	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.295	0.030	0.024	0.0	-1	5	0.747	0.015	4.409	4.409	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.315	0.052	0.024	0.0	1	100	0.728	0.013	4.551	4.552	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.316	0.066	0.024	0.0	-1	100	0.759	0.014	4.368	4.369	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.635	0.073	0.022	0.0	1	5	0.715	0.007	5.085	5.086	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.705	0.114	0.022	0.0	1	1	0.751	0.015	4.931	4.932	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.001	0.018	0.0	-1	1	0.003	0.002	0.298	0.336	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.030	0.0	-1	5	0.002	0.002	0.258	0.323	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.000	0.000	0.034	0.0	1	100	0.001	0.000	0.565	0.576	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.000	0.000	0.034	0.0	-1	100	0.001	0.000	0.571	0.578	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.000	0.000	0.034	0.0	1	5	0.001	0.000	0.603	0.604	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.000	0.000	0.034	0.0	1	1	0.001	0.000	0.628	0.629	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.801	0.921	0.000	0.001	-1	1	0.118	0.002	6.789	6.790	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.001	0.000	0.002	-1	1	0.000	0.000	9.077	9.529	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.043	0.468	0.000	0.001	-1	5	0.208	0.002	5.026	5.026	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	-1	5	0.000	0.000	7.219	7.652	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	5.536	0.393	0.000	0.006	1	100	0.647	0.021	8.558	8.563	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	1	100	0.001	0.000	3.511	3.778	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	3.100	0.220	0.000	0.003	-1	100	0.621	0.021	4.991	4.994	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.005	0.001	0.000	0.005	-1	100	0.001	0.000	6.107	6.426	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.634	0.260	0.000	0.002	1	5	0.210	0.009	7.798	7.806	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.000	0.000	0.002	1	5	0.000	0.000	3.924	4.156	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.881	0.251	0.000	0.001	1	1	0.112	0.002	7.854	7.856	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	0.000	0.001	1	1	0.000	0.000	3.369	3.589	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.022	0.011	0.001	0.000	-1	1	0.000	0.000	61.428	61.777	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.001	0.000	0.002	-1	1	0.000	0.000	30.020	31.305	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.021	0.001	0.001	0.000	-1	5	0.001	0.000	35.968	35.997	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	5	0.000	0.000	22.067	23.042	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.032	0.009	0.000	0.000	1	100	0.004	0.000	7.715	7.727	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	100	0.000	0.000	5.798	6.048	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.036	0.010	0.000	0.000	-1	100	0.005	0.001	7.640	7.992	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.002	0.000	0.002	-1	100	0.000	0.000	27.648	29.092	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.018	0.000	0.001	0.000	1	5	0.001	0.000	29.430	29.511	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.000	0.000	0.000	0.000	1	5	0.000	0.000	6.430	6.820	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.016	0.000	0.001	0.000	1	1	0.000	0.000	46.103	46.210	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.000	0.000	0.000	0.000	1	1	0.000	0.000	6.293	6.629	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.477	0.071	30	0.034	0.0	random	0.369	0.022	1.292	1.295	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.534	0.022	30	0.030	0.0	k-means++	0.400	0.019	1.334	1.335	See	See
6	KMeans_tall	fit	1000000	1000000	100	5.544	0.206	30	0.144	0.0	random	2.598	0.032	2.134	2.134	See	See
9	KMeans_tall	fit	1000000	1000000	100	5.720	0.032	30	0.140	0.0	k-means++	2.946	0.364	1.942	1.956	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.010	0.000	random	0.0	0.0	7.855	11.910	See	See
2	KMeans_tall	predict	1000000	1	2	0.001	0.000	30	0.000	0.001	random	0.0	0.0	8.072	11.967	See	See
4	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.009	0.000	k-means++	0.0	0.0	13.818	15.660	See	See
5	KMeans_tall	predict	1000000	1	2	0.001	0.000	30	0.000	0.001	k-means++	0.0	0.0	12.611	13.212	See	See
7	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.488	0.000	random	0.0	0.0	6.576	7.135	See	See
8	KMeans_tall	predict	1000000	1	100	0.001	0.000	30	0.001	0.001	random	0.0	0.0	12.480	13.044	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.468	0.000	k-means++	0.0	0.0	6.783	7.335	See	See
11	KMeans_tall	predict	1000000	1	100	0.001	0.000	30	0.001	0.001	k-means++	0.0	0.0	14.354	14.811	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

WARNING! Mismatch between validation scores for 4 predictions (50.0%). See below.

	estimator	function	n_samples_train	n_samples	n_features	adjusted_rand_score_sklearn	adjusted_rand_score_sklearnex	diff_adjusted_rand_scores
1	KMeans_short	predict	10000	1000	2	0.000126	-0.000965	0.001090
4	KMeans_short	predict	10000	1000	2	0.001245	-0.000750	0.001995
7	KMeans_short	predict	10000	1000	100	0.278733	0.293767	0.015034
10	KMeans_short	predict	10000	1000	100	0.317011	0.256968	0.060044

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.071	0.001	20	0.002	0.0	random	0.026	0.003	2.776	2.794	See	See
3	KMeans_short	fit	10000	10000	2	0.207	0.003	20	0.001	0.0	k-means++	0.076	0.001	2.721	2.721	See	See
6	KMeans_short	fit	10000	10000	100	0.189	0.003	20	0.042	0.0	random	0.104	0.003	1.825	1.826	See	See
9	KMeans_short	fit	10000	10000	100	0.557	0.007	20	0.014	0.0	k-means++	0.300	0.012	1.859	1.860	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.010	0.000	random	0.000	0.0	3.849	3.855	See	See
2	KMeans_short	predict	10000	1	2	0.001	0.0	20	0.000	0.001	random	0.000	0.0	12.980	13.338	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.010	0.000	k-means++	0.000	0.0	3.737	3.752	See	See
5	KMeans_short	predict	10000	1	2	0.001	0.0	20	0.000	0.001	k-means++	0.000	0.0	12.833	13.264	See	See
7	KMeans_short	predict	10000	1000	100	0.002	0.0	20	0.344	0.000	random	0.001	0.0	2.438	2.451	See	See
8	KMeans_short	predict	10000	1	100	0.001	0.0	20	0.001	0.001	random	0.000	0.0	9.404	9.530	See	See
10	KMeans_short	predict	10000	1000	100	0.002	0.0	20	0.352	0.000	k-means++	0.001	0.0	2.393	2.406	See	See
11	KMeans_short	predict	10000	1	100	0.001	0.0	20	0.001	0.001	k-means++	0.000	0.0	9.635	10.016	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

WARNING! Mismatch between validation scores for 2 predictions (50.0%). See below.

	estimator	function	n_samples_train	n_samples	n_features	accuracy_score_sklearn	accuracy_score_sklearnex	diff_accuracy_scores
1	LogisticRegression	predict	1000000	1000	100	0.56	0.55	0.01
4	LogisticRegression	predict	1000	100	10000	0.35	0.28	0.07

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	10.782	0.480	[20]	0.074	0.000	1.924	0.017	5.604	5.604	See	See
3	LogisticRegression	fit	1000	1000	10000	1.007	0.728	[26]	0.079	0.001	0.955	0.034	1.054	1.054	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.000	0.0	[20]	1.998	0.0	0.001	0.001	0.577	1.125	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.0	[20]	0.016	0.0	0.000	0.000	0.370	0.377	See	See
4	LogisticRegression	predict	1000	100	10000	0.002	0.0	[26]	5.011	0.0	0.003	0.000	0.555	0.558	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.0	[26]	1.010	0.0	0.001	0.000	0.128	0.128	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

WARNING! Mismatch between validation scores for 1 prediction (25.0%). See below.

	estimator	function	n_samples_train	n_samples	n_features	r2_score_sklearn	r2_score_sklearnex	diff_r2_scores
1	Ridge	predict	1000	1000	10000	0.082567	0.122191	0.039624

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.172	0.003	0.465	0.0	0.181	0.001	0.952	0.952	See	See
3	Ridge	fit	1000000	1000000	100	1.128	0.082	0.709	0.0	0.303	0.254	3.723	4.856	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.011	0.0	7.076	0.0	0.018	0.0	0.625	0.625	See	See
2	Ridge	predict	1000	1	10000	0.000	0.0	1.301	0.0	0.000	0.0	0.656	0.719	See	See
4	Ridge	predict	1000000	1000	100	0.000	0.0	5.439	0.0	0.000	0.0	0.472	0.710	See	See
5	Ridge	predict	1000000	1	100	0.000	0.0	0.017	0.0	0.000	0.0	0.570	0.616	See	See